While we train our model on colored Imagenet data, we observe that in practice this approach works well on reconstructing grayscale images as well. We compare our reconstruction with that of RIDE-CS [1]}, which uses the autoregressive model RIDE [2]} as image prior. In Figure REF , we compare the reconstruction of a grayscale image from Single Pixel Camera measurements using our approach and RIDE-CS for \(15\%\)  measurement rate. The reconstruction obtained from our approach is better than that of RIDE-CS. This is because we use PixelCNN++ which is a deeper network than RIDE and hence has better representation power. Also, the running time of our approach (\(\sim \)  5 minutes ) is much less than that of RIDE-CS (\(\sim \)  30 minutes). Our approach is CNN based and hence can be parallelized over multiple GPUs while RIDE-CS relies on a network of spatial LSTMs which are tough to parallelize.
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